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UDAMA: Unsupervised Domain Adaptation through Multi-discriminator
  Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction

UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction

31 July 2023
Yu Wu
Dimitris Spathis
Hong Jia
I. Perez-Pozuelo
Tomas I. Gonzales
S. Brage
N. Wareham
Cecilia Mascolo
ArXivPDFHTML

Papers citing "UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction"

3 / 3 papers shown
Title
M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with
  Multi-Branch Adversarial Training
M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training
L. Meegahapola
Hamza Hassoune
D. Gática-Pérez
40
9
0
26 Apr 2024
Model Evaluation, Model Selection, and Algorithm Selection in Machine
  Learning
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
S. Raschka
83
764
0
13 Nov 2018
Domain-Adversarial Training of Neural Networks
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin
E. Ustinova
Hana Ajakan
Pascal Germain
Hugo Larochelle
François Laviolette
M. Marchand
Victor Lempitsky
GAN
OOD
177
9,342
0
28 May 2015
1